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        "author": "Microsoft Corporation",
        "author_email": "winml@microsoft.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
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        "description": "WinMLTools provide following tools for Windows ML:\n\nModel Conversion\n################\n\nWinMLTools enables you to convert models from different machine \nlearning toolkits into `ONNX <https://onnx.ai>`_ for use with Windows ML. \nCurrently the following toolkits are supported:\n\n* apple CoreML\n* keras\n* scikit-learn\n* lightgbm\n* xgboost\n* libSVM\n* tensorflow (experimental)\n\nHere is a simple example to convert a Core ML model:\n\n::\n\n    from coremltools.models.utils import load_spec\n    from winmltools import convert_coreml\n    model_coreml = load_spec('example.mlmodel')\n    model_onnx = convert_coreml(model_coreml, 7, name='ExampleModel')\n\nPost Training Weight Quantization\n#################################\n\nWinMLTools provides quantization tool to reduce the memory footprint of the model.\n\nHere is an example to convert an ONNX model to a quantized ONNX model:\n\n::\n\n    import winmltools\n\n    model = winmltools.load_model('model.onnx')\n    quantized_model = winmltools.quantize(model, per_channel=True, nbits=8, use_dequantize_linear=True)\n    winmltools.save_model(quantized_model, 'quantized.onnx')\n\n\nDependencies\n============\n\nIn order to convert from different toolkits, you may have to install the following packages for different converters: \n\n+--------------+----------------------------------------------------------------------------------------------------------------------------------------------------+\n| Toolkit      | Source                                                                                                                                             |\n+==============+====================================================================================================================================================+\n| keras        | https://pypi.org/project/Keras                                                                                                                     |\n+--------------+----------------------------------------------------------------------------------------------------------------------------------------------------+\n| tensorflow   | https://pypi.org/project/tensorflow                                                                                                                |\n+--------------+----------------------------------------------------------------------------------------------------------------------------------------------------+\n| scikit-learn | https://pypi.org/project/scikit-learn                                                                                                              |\n+--------------+----------------------------------------------------------------------------------------------------------------------------------------------------+\n| lightgbm     | https://pypi.org/project/lightgbm                                                                                                                  |\n+--------------+----------------------------------------------------------------------------------------------------------------------------------------------------+\n| xgboost      | https://pypi.org/project/xgboost                                                                                                                   |\n+--------------+----------------------------------------------------------------------------------------------------------------------------------------------------+\n| libsvm       | You can download libsvm wheel from various web sources. One example can be found here: https://www.lfd.uci.edu/~gohlke/pythonlibs/#libsvm          |\n+--------------+----------------------------------------------------------------------------------------------------------------------------------------------------+\n| coremltools  | Currenlty coreml does not distribute coreml packaging on windows. You can install from source: pip install git+https://github.com/apple/coremltools|\n+--------------+----------------------------------------------------------------------------------------------------------------------------------------------------+\n\n\nFor more information on WinMLTools, you can go to `Convert ML models to ONNX with WinMLTools\n<https://docs.microsoft.com/en-us/windows/ai/convert-model-winmltools>`_\n\nLicense\n=======\n\nMIT License\n\n\n",
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        "license": "MIT License",
        "maintainer": "",
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        "name": "winmltools",
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        "summary": "Converts Machine Learning models to ONNX for use in Windows ML",
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